Payments for Watershed Protection Services: Emerging Lessons from the Philippines
Bibliographic record
Abstract
There is growing interest on payments for ecosystem services (PES) in developing countries including the Philippines. Watersheds have been degraded through deforestation and subsequent conversion to other land cover, principally for agriculture. In the last decade, several Payments for Watershed Services schemes have been implemented and this paper is an attempt to assess the form of incentives or rewards that have been provided to upland communities in a number of sites under different management leadership in the Philippines. We reviewed four cases specifically related to watershed services in the: 1) Bakun Watershed, 2) Maasin Watershed, 3) Sibuyan Watershed, and 4) Baticulan Watershed. The case studies of varying stages of implementation has shown that the chances of success of PES schemes in promoting watershed conservation and rehabilitation as well as in improving the livelihoods of upland communities is constrained by incomplete information and knowledge about the interaction between ecosystem properties and provision of services, and the difficulty in establishing voluntary participation and conditionality of payments. In this paper, we argued that institutions may enable or hinder the successful implementation of PES. The role of the local government as intermediaries is crucial in the process of establishing PES more particularly in the information dissemination and education of the key stakeholders. The case studies also showed how PES programs are reinforced by the presence of non-government organizations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".